library(reticulate)
use_condaenv("glmhmm")
ERROR: The requested version of Python
('/Users/daniellinares/opt/anaconda3/envs/glmhmm/bin/python')
cannot be used, as another version of Python
('/Library/Frameworks/Python.framework/Versions/3.10/bin/python3')
has already been initialized. Please restart the
R session if you need to attach reticulate to a
different version of Python.
Error in use_python(python, required = required) : 
  failed to initialize requested version of Python
import numpy as np
import numpy.random as npr
import matplotlib.pyplot as plt
import ssm 
library(tidyverse)
── Attaching core tidyverse packages ───────────────────
✔ dplyr     1.1.2     ✔ readr     2.1.4
✔ forcats   1.0.0     ✔ stringr   1.5.0
✔ ggplot2   3.4.2     ✔ tibble    3.2.1
✔ lubridate 1.9.2     ✔ tidyr     1.3.0
✔ purrr     1.0.1     ── Conflicts ────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the ]8;;http://conflicted.r-lib.org/conflicted package]8;; to force all conflicts to become errors
library(broom)
library(dplyr)
library(R.matlab)
R.matlab v3.7.0 (2022-08-25 21:52:34 UTC) successfully loaded. See ?R.matlab for help.

Attaching package: ‘R.matlab’

The following objects are masked from ‘package:base’:

    getOption, isOpen
library(cowplot)

Attaching package: ‘cowplot’

The following object is masked from ‘package:lubridate’:

    stamp
list.files("R", full.names = TRUE) |> 
  walk(source)
load("RData/glm_hmm1_seeds.RData")
load("RData/glm_hmm2_seeds.RData")
load("RData/glm_hmm3_seeds.RData")

Renames the states according to their values (so similar states between subjects can be identified)

# glm_hmm1 <- glm_hmm1_seeds %>% 
#     mutate(fit = list(glm_hmm_extract_best(fit))) %>%
#     order_states_glm_hmm()
# 
# glm_hmm1
# 
# save(glm_hmm1, file = "RData/glm_hmm1.RData")
# glm_hmm3 <- glm_hmm3_seeds %>%
#   mutate(fit = list(glm_hmm_extract_best(fit))) %>%
#   order_states_glm_hmm()
# 
# glm_hmm3
# 
# save(glm_hmm3, file = "RData/glm_hmm3.RData")
glm_hmm4 <- glm_hmm4_seeds %>%
  mutate(fit = list(glm_hmm_extract_best(fit))) %>%
  order_states_glm_hmm()
Joining with `by = join_by(subject, state)`
load("RData/glm_hmm1.RData")
load("RData/glm_hmm2.RData")
load("RData/glm_hmm3.RData")

———————————————————————

MODEL COMPARISON


liks de cada modelo

DL:

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